Beyond Supervised Learning: A Computer Vision Perspective

Lovish Chum, Anbumani Subramanian, Vineeth N. Balasubramanian, C. V. Jawahar


Fully supervised deep learning-based methods have created
a profound impact in various fields of computer science. Compared to
classical methods, supervised deep learning-based techniques face
scalability issues as they require huge amounts of labeled data and,
more significantly, are unable to generalize to multiple domains and
tasks. In recent years, a lot of research has been targeted towards
addressing these issues within the deep learning community. Although
there have been extensive surveys on learning paradigms such as semisupervised and unsupervised learning, there are a few timely reviews
after the emergence of deep learning. In this paper, we provide an
overview of the contemporary literature surrounding alternatives to fully
supervised learning in the deep learning context. First, we summarize
the relevant techniques that fall between the paradigm of supervised
and unsupervised learning. Second, we take autonomous navigation
as a running example to explain and compare different models. Finally,
we highlight some shortcomings of current methods and suggest future


Deep learning, Synthetic data, Domain adaptation, Weakly supervised learning, Few-shot learning, Self-supervised learning

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